Additive Manufacturing and Generative Design Put Manufacturing Operations Management in the Spotlight as Hybrid Manufacturing Heats Up

By 2030, the number of production-ready additive manufacturing platforms that ship each year will increase more than tenfold (more than US$325 billion in hardware and related systems revenue). These systems will produce more than US$360 billion worth of parts and end products each year (up from US$6.8 billion today) and nearly US$2 trillion in sum by the end of the next decade (MD-AMMT-101). These systems will need to work with the ones that came before them. Today, most do not.

The field of Manufacturing Operations Management (MOM) is, like the systems it supports, evolving from manufacturing execution (data acquisition) to digitalization (data synchronization and contextualization) and, soon, higher levels of intelligence (data augmentation). Companies like PTC, which acquired Frustum at the end of 2018 (after the US$1 Billion Rockwell tie-up) and Siemens, which acquired low-code developer Mendix on the heels of its of Mentor Graphics pickup (in 2018 and 2017, respectively), have started to show their cards, and there are several key takeaways the industrial industry must heed.

All the major automotive and aerospace vendors used to develop their own manufacturing execution systems (MES), but, even then, standardization was a problem. Different processes drove different results. It didn’t matter if it was one factory in a network of two or five factories in a network of twenty--if the molding and QA processes weren’t the same, neither were the end products. While most of these companies have since moved away from their proprietary systems in favor of more user-friendly off-the-shelf MES solutions, the propensity to standardize on results before process needs to be reversed. Hybrid manufacturing will command it.

For technologies like additive manufacturing and generative design, this is clear: MES is the solution to the MOM orchestration problem the industry doesn’t yet know it has. Here’s why: When you introduce compelling and radically new technologies, especially in large, established, and/or well understood markets such as manufacturing, there is a long transitionary period during which high CAPEX items can be amortized through operations. We are now entering this period, but it’s for these same reasons that MES thinking needs to be applied to MOM implementations; the core of MES is advanced planning and scheduling, and the core of what MOM needs and will become is orchestration based on the availability and effectiveness of a certain process.

Simulation and virtualization shorten cycles and improve yield. Companies with end-to-end ownership of both digital (ThingWorx, Mindsphere, Ability, etc.) and physical systems (PTC/Rockwell, Siemens, and ABB, respectively) have a leg up in terms of integration and implementation, but the reality is that not everyone subscribes to a single hardware/software ecosystem. This is why there are integrators and also why there is a big push in the industrial platform space--to minimize the number of vendors and touchpoints, yet maximize the number of assets with which they interoperate. Managing the associated technical and business complexity puts MOM in the spotlight.

The quickest ROI for MOM implementations is (in order):

1. Scheduling: Usually in the vicinity of six to nine months ROI for a manufacturing firm, advanced planning and scheduling systems replace paper- and excel-based processes with a purpose-built app. The importance of timing and alignment in manufacturing operations management are what make scheduling the first item industrial firms must tackle in their digital transformation journey.
2. Intelligence and performance analysis: What is the deployment model for new product or factory orientations? How easy is it to standardize, integrate, and manage change? This is where further analysis of the factory network happens--not just for the underlying technologies, but how they interoperate relative to the operations of the business (IT/OT convergence).
3. Quality: There is a low implementation level of quality catalogues, which can be generally thought of as databases designed to match and identify defects based on standard output. In cases where the data exist (often larger scale or established production facilities), it isn’t always accessible. For MOM, Industry 4.0, and the future of manufacturing, quality is really about a set of standards and control that apply to not only the result, but also the processes and interdependent systems that make it happen.